Overview

When you publish manuscripts based on data generated at our facility, we would greatly appreciate an acknowledgement of our efforts. Please cite our facility as follows (for example):

Basic processing of the raw data were performed by the University of Illinois at Chicago Research Informatics Core (UICRIC).

We adhere to a general policy for acknowledgements and authorship as established by the Association for Biomolecular Resource Facilities (ABRF) , and we support the following statement from the ABRF.

The existence of core facilities depends in part on proper acknowledgment in publications. This is an important metric of the value of most core facilities. Proper acknowledgment of core facilities enables them to obtain financial and other support so that they may continue to provide their essential services in the best ways possible. It also helps core personnel to advance in their careers, adding to the overall health of the core facility.

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File Description Type
taxa_raw_counts.xlsx Excel spreadsheet of taxonomic summaries from phylum to species level - raw sequence counts result
taxa_raw_counts.zip ZIP archive of taxonomic summaries from phylum to species level - raw sequence counts result
kegg_raw_counts.xlsx Excel spreadsheet of functional gene (KEGG ortholog) summaries - raw sequence counts result
kegg_raw_counts.zip ZIP archive of functional gene (KEGG ortholog) summaries - raw sequence counts result
metagenomic_assembly.zip ZIP compressed FASTA file of all contigs in the assembly result
contig_counts.txt Raw read counts for assembled contigs result
orf_counts.txt Raw read counts for predicted ORFs result
contig_data.txt Details and normalized read counts (RPKM) of the contigs result
contig_annotations.txt Gene annotations of predicted ORFs, as TSV file result
annotation_results.zip ZIP file of annotation results as and GFF format with FASTA files of nucleotide and amino acid sequences for each ORF result
bins_data.txt Details the metagenomic bins result

Sample list
Sample OriginalID
SRR22405441 SRR22405441
SRR22405442 SRR22405442
SRR22405445 SRR22405445
SRR22405453 SRR22405453
SRR22405460 SRR22405460
SRR22405461 SRR22405461
SRR22405467 SRR22405467
SRR22405532 SRR22405532
SRR22405538 SRR22405538
SRR22405544 SRR22405544

Method: cutadaptMartin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 17(1):10-12. doi:https://doi.org/10.14806/ej.17.1.200 (version: 4.4)

Sequencing trimming using cutadapt

Custom Parameters
  • --report = minimal
  • -m = 70
  • --trim-n
  • --max-n = 1
  • -b = file:/mmfs1/projects/rrc_shared/common/references/adapters/illumina_adapters.fa
Method: Quality trimming

Quality trimming based on quality threshold and length parameters.

Custom Parameters
  • min length = 70
Method: Adapter trimming

Adapter/primer sequences were trimmed from the reads.

Custom Parameters
  • adapter = AGATCGGAAGAGCACACGTCTGAACTCCAGTCA
  • adapter = AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
  • adapter = TGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
  • adapter = ACACTCTTTCCCTACACGACGCTCTTCCGATCT
Method: Abiguous nucleotide trimming

Ambiguous nucleotides (N) were trimmed from the ends and reads with internal ambiguous nucleotides were discarded.

Custom Parameters
  • max-n = 1

Figure 1 . Trimming results

Table 1 . Trimming statistics

Table 1 . Trimming statistics
Sample Trim passed Ambig dropped Trim filter dropped Trim length dropped Percent passed Mean Length Mean Trimmed Length Quality trimmed bp
SRR22405441 998916 1084 0 0 99.89% 302.0 301.6 0
SRR22405442 999093 907 0 0 99.91% 302.0 301.6 0
SRR22405445 999098 902 0 0 99.91% 302.0 301.6 0
SRR22405453 999209 791 0 0 99.92% 302.0 301.6 0
SRR22405460 999065 935 0 0 99.91% 302.0 301.6 0
SRR22405461 999104 896 0 0 99.91% 302.0 301.6 0
SRR22405467 999137 863 0 0 99.91% 302.0 301.6 0
SRR22405532 999248 752 0 0 99.92% 302.0 301.6 0
SRR22405538 999247 753 0 0 99.92% 302.0 301.6 0
SRR22405544 999069 931 0 0 99.91% 302.0 301.6 0

  • Denovo genome assembly using short read sequence data

Method: Ultra-fast and memory-efficient (meta-)genome assemblerLi, D., Liu, C-M., Luo, R., Sadakane, K., and Lam, T-W., (2015) MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics, https://doi.org/10.1093/bioinformatics/btv033Li, D., Luo, R., Liu, C.M., Leung, C.M., Ting, H.F., Sadakane, K., Yamashita, H. and Lam, T.W., 2016. MEGAHIT v1.0: A Fast and Scalable Metagenome Assembler driven by Advanced Methodologies and Community Practices. Methods.

MEGAHIT is an ultra-fast and memory-efficient NGS assembler. It is optimized for metagenomes, but also works well on generic single genome assembly (small or mammalian size) and single-cell assembly.

Figure 1 . Coverage plot

Table 1 . Assembly summaries by stage

Table 1 . Assembly summaries by stage
Stage Count TotalLength Longest N50 N75 N90 L50 L75 L90
Raw assembly 98768 133001436 386035 4524 773 423 3296 25013 60961
Entropy filtered 17826 93469303 386035 17101 3588 1593 841 4193 10284
Alignment filtered 17826 93469303 386035 17101 3588 1593 841 4193 10284

Table 2 . Basic contig statistics, all contigs

Table 2 . Basic contig statistics, all contigs
#ID length GC coverage S_2mer S_3mer S_4mer
k141_76796 386035 51.891 25.03 0.995 0.992 0.988
k141_76622 349967 48.068 71.95 0.995 0.992 0.988
k141_59322 343211 43.778 14.91 0.991 0.987 0.984
k141_20763 342700 47.468 52.48 0.995 0.992 0.988
k141_39922 334399 46.144 42.69 0.995 0.992 0.988
k141_13684 299237 42.963 103.05 0.989 0.986 0.983
k141_73726 298100 46.238 47.34 0.994 0.991 0.987
k141_24136 297381 42.899 23.71 0.989 0.985 0.982
k141_58801 294754 53.046 25.54 0.995 0.991 0.988
k141_79554 290324 43.567 15.98 0.992 0.988 0.985
k141_68893 284177 59.188 15.93 0.980 0.976 0.971
k141_62980 260876 51.135 89.65 0.996 0.993 0.990
k141_75130 260643 47.584 52.42 0.996 0.992 0.989
k141_80951 254966 56.175 44.12 0.985 0.977 0.971
k141_73145 254313 51.474 85.44 0.996 0.993 0.989
k141_72709 250564 60.446 19.12 0.977 0.972 0.968
k141_98708 248942 44.240 18.86 0.991 0.986 0.982
k141_78261 247234 49.724 82.74 0.996 0.993 0.990
k141_43615 244785 45.550 45.18 0.993 0.990 0.986
k141_80866 237758 59.367 18.46 0.980 0.976 0.971
k141_28842 231031 43.386 20.93 0.990 0.986 0.983
k141_87164 215400 45.105 48.75 0.991 0.988 0.984
k141_53271 212672 45.597 23.28 0.994 0.989 0.985
k141_68261 212292 42.070 14.21 0.988 0.985 0.982
k141_1964 206776 52.656 24.64 0.994 0.990 0.986
k141_33256 201433 47.380 53.60 0.996 0.992 0.989
k141_48872 197638 42.358 15.04 0.988 0.985 0.982
k141_81796 187223 52.728 25.19 0.995 0.990 0.987
k141_88700 185271 47.826 44.07 0.996 0.993 0.989
k141_47485 183654 59.867 17.78 0.979 0.974 0.970
k141_25275 181053 42.652 23.43 0.988 0.984 0.981
k141_26205 181034 55.255 36.49 0.987 0.979 0.973
k141_16269 179431 59.056 18.45 0.980 0.975 0.970
k141_33897 176101 52.275 24.66 0.994 0.990 0.986
k141_45061 175853 45.180 13.75 0.995 0.991 0.987
k141_94778 174227 44.188 16.41 0.991 0.987 0.983
k141_62931 171919 52.663 64.31 0.996 0.993 0.989
k141_88682 171703 44.804 107.49 0.992 0.989 0.986
k141_95298 170655 55.980 15.11 0.991 0.986 0.982
k141_36328 169718 56.888 15.50 0.989 0.985 0.980
k141_55934 167063 43.125 96.17 0.988 0.985 0.981
k141_76713 166254 54.122 14.72 0.994 0.991 0.987
k141_40528 165793 43.850 24.56 0.990 0.985 0.982
k141_91618 164123 44.333 20.29 0.991 0.986 0.983
k141_79956 163397 43.819 15.28 0.991 0.987 0.984
k141_43116 159435 46.194 57.08 0.994 0.990 0.987
k141_10798 158681 42.615 17.22 0.988 0.984 0.981
k141_80995 158391 55.843 15.79 0.988 0.984 0.980
k141_64596 158147 42.932 89.54 0.989 0.986 0.983
k141_22008 157936 51.005 20.85 0.994 0.991 0.987
k141_33425 156971 59.596 14.16 0.979 0.974 0.970
k141_91139 155923 43.156 23.90 0.989 0.985 0.981
k141_52536 154384 46.888 51.19 0.995 0.992 0.988
k141_3747 153662 43.432 90.19 0.990 0.987 0.984
k141_89123 153142 42.690 15.94 0.989 0.984 0.981
k141_32389 152608 42.628 116.29 0.989 0.986 0.983
k141_81018 151527 43.632 15.37 0.990 0.987 0.983
k141_46214 151251 45.147 15.59 0.993 0.989 0.985
k141_27196 150924 44.891 19.94 0.993 0.989 0.985
k141_96249 149946 59.731 15.46 0.979 0.974 0.969
k141_13535 149705 42.163 102.80 0.988 0.985 0.981
k141_44155 149580 47.624 11.10 0.996 0.993 0.990
k141_8629 148489 44.524 16.69 0.992 0.987 0.983
k141_12912 146001 43.604 107.68 0.990 0.987 0.983
k141_72815 142594 51.029 82.99 0.995 0.992 0.988
k141_90142 142375 41.815 106.17 0.987 0.984 0.981
k141_99945 142287 43.939 98.40 0.991 0.988 0.984
k141_49643 141763 42.184 105.33 0.987 0.984 0.980
k141_55038 136465 56.687 39.04 0.985 0.976 0.970
k141_21442 135421 59.063 17.53 0.979 0.974 0.970
k141_50679 135091 56.597 17.36 0.987 0.982 0.979
k141_26230 133070 49.678 75.76 0.995 0.992 0.988
k141_84469 132617 45.896 22.78 0.993 0.990 0.987
k141_94955 132166 43.645 105.77 0.989 0.986 0.982
k141_14515 131559 55.999 42.28 0.985 0.977 0.971
k141_34789 130310 40.701 93.17 0.984 0.981 0.978
k141_57446 130099 43.392 22.81 0.990 0.986 0.982
k141_45960 128383 41.062 21.22 0.984 0.980 0.977
k141_83366 128221 50.585 18.60 0.993 0.989 0.985
k141_26590 123846 43.918 17.95 0.992 0.988 0.985
k141_90685 123773 51.598 20.19 0.992 0.988 0.984
k141_51840 122240 58.581 15.27 0.981 0.977 0.972
k141_57014 122187 49.666 17.58 0.992 0.988 0.984
k141_64137 122120 50.239 19.00 0.994 0.990 0.986
k141_54940 121746 50.261 28.09 0.996 0.992 0.989
k141_10440 121245 49.061 87.37 0.997 0.994 0.990
k141_43267 121088 47.904 50.61 0.996 0.993 0.989
k141_63716 119854 56.956 15.03 0.987 0.984 0.979
k141_57996 119376 41.513 21.65 0.985 0.982 0.978
k141_61276 119182 46.954 21.94 0.995 0.992 0.989
k141_51931 116680 49.949 17.94 0.995 0.992 0.989
k141_2796 115178 42.488 18.40 0.988 0.985 0.981
k141_23606 113096 52.944 86.43 0.995 0.992 0.987
k141_51271 112899 50.967 82.12 0.995 0.992 0.987
k141_26306 111928 42.784 19.77 0.988 0.983 0.980
k141_84762 111178 57.975 13.67 0.985 0.981 0.977
k141_57522 110986 58.216 13.63 0.983 0.978 0.974
k141_53107 110719 42.642 23.86 0.989 0.985 0.982
k141_58297 109773 47.991 22.59 0.995 0.991 0.988
k141_86083 108052 47.107 50.54 0.995 0.991 0.987
k141_52132 106536 43.321 114.54 0.990 0.986 0.983
k141_5185 105585 53.258 26.03 0.996 0.992 0.988
k141_3574 105582 42.673 93.89 0.988 0.985 0.982
k141_83948 105003 53.173 25.54 0.995 0.991 0.987
k141_24846 104179 48.006 49.05 0.996 0.992 0.987
k141_37907 104038 51.698 87.93 0.997 0.994 0.990
k141_59966 103194 43.400 22.87 0.991 0.987 0.985
k141_96042 103058 42.712 21.30 0.988 0.983 0.980
k141_16623 102470 49.339 77.06 0.996 0.993 0.989
k141_95816 102259 44.355 12.85 0.993 0.989 0.985
k141_28755 102246 51.984 24.35 0.996 0.992 0.989
k141_71278 100834 46.324 47.84 0.993 0.989 0.985
k141_81778 100649 44.045 18.48 0.991 0.986 0.983
k141_43117 100016 42.449 109.85 0.987 0.984 0.981
k141_55162 98611 41.167 19.03 0.986 0.982 0.979
k141_38172 98169 49.635 22.71 0.996 0.993 0.990
k141_85651 96475 43.396 22.20 0.990 0.986 0.982
k141_82090 96469 51.144 19.29 0.993 0.990 0.986
k141_13630 95969 54.683 35.01 0.988 0.977 0.971
k141_8569 95611 42.234 20.42 0.986 0.982 0.979
k141_15332 93673 44.244 15.48 0.993 0.989 0.985
k141_46494 92386 54.760 16.15 0.992 0.987 0.983
k141_79209 92097 46.030 44.26 0.993 0.990 0.986
k141_66732 91819 51.926 25.07 0.994 0.990 0.986
k141_21079 91155 45.924 48.94 0.992 0.988 0.984
k141_13736 90938 44.397 17.11 0.990 0.985 0.981
k141_5631 90881 48.547 94.12 0.996 0.993 0.989
k141_90296 90510 56.724 41.20 0.983 0.974 0.968
k141_34098 90004 56.279 15.62 0.990 0.985 0.981
k141_35975 89692 53.772 12.18 0.994 0.991 0.988
k141_75747 89188 44.223 91.00 0.991 0.988 0.985
k141_28050 88763 57.652 15.71 0.988 0.984 0.979
k141_86549 87926 42.047 111.37 0.987 0.984 0.981
k141_7211 87173 44.237 14.92 0.992 0.988 0.984
k141_80346 86661 43.464 16.91 0.990 0.985 0.982
k141_97217 86515 53.057 23.06 0.993 0.989 0.985
k141_62364 86454 41.184 12.34 0.986 0.982 0.979
k141_3009 86200 51.450 75.86 0.995 0.992 0.987
k141_1311 85903 39.908 18.90 0.980 0.977 0.974
k141_92677 85546 54.970 15.49 0.988 0.983 0.979
k141_56954 85296 55.862 36.58 0.985 0.977 0.970
k141_22015 85138 45.083 110.86 0.992 0.988 0.985
k141_58624 84646 51.024 24.16 0.996 0.992 0.988
k141_77146 84614 35.912 49.60 0.963 0.958 0.954
k141_86556 84206 41.352 90.92 0.986 0.984 0.980
k141_31425 83402 43.096 21.65 0.989 0.984 0.979
k141_66283 82819 55.090 34.75 0.991 0.987 0.984
k141_25112 81974 55.830 34.04 0.986 0.977 0.971
k141_97367 81821 42.415 22.68 0.987 0.983 0.979
k141_5988 81384 64.865 13.64 0.961 0.957 0.951
k141_91557 81285 53.550 22.94 0.992 0.989 0.984
k141_78591 80719 42.082 14.26 0.988 0.984 0.981
k141_42611 80405 58.367 15.30 0.981 0.976 0.971
k141_62001 80387 56.054 14.66 0.986 0.982 0.978
k141_3781 80201 40.598 17.87 0.983 0.980 0.977
k141_89298 80066 41.920 96.03 0.986 0.983 0.980
k141_97371 78463 45.776 18.97 0.994 0.989 0.984
k141_92065 78312 43.320 12.13 0.990 0.987 0.983
k141_51270 78264 46.719 51.39 0.995 0.991 0.987
k141_85999 78120 41.852 22.71 0.986 0.981 0.978
k141_81552 77910 45.975 15.85 0.993 0.989 0.986
k141_55796 77458 44.700 18.73 0.993 0.989 0.985
k141_25568 76803 49.157 44.86 0.996 0.993 0.988
k141_19045 76740 40.658 11.50 0.984 0.981 0.979
k141_89380 76728 57.822 42.33 0.983 0.974 0.968
k141_31275 76690 43.735 15.35 0.990 0.987 0.983
k141_90547 76151 55.318 43.19 0.987 0.978 0.972
k141_3603 75988 42.984 111.35 0.989 0.986 0.983
k141_55711 75523 56.493 37.17 0.986 0.976 0.970
k141_50966 75121 47.984 22.32 0.995 0.992 0.989
k141_68058 75099 53.561 13.97 0.993 0.988 0.984
k141_63582 74975 49.931 96.84 0.997 0.995 0.991
k141_26510 74893 56.276 42.14 0.983 0.974 0.968
k141_27478 74523 43.733 15.09 0.991 0.987 0.983
k141_81741 74238 41.145 24.36 0.984 0.979 0.976
k141_55665 74036 46.715 15.21 0.996 0.992 0.989
k141_26190 72914 54.493 48.25 0.990 0.981 0.975
k141_9227 71637 43.108 112.68 0.990 0.987 0.984
k141_46279 71566 59.732 17.27 0.979 0.974 0.968
k141_2573 71295 44.127 17.82 0.989 0.983 0.979
k141_31680 69867 54.332 40.17 0.988 0.981 0.975
k141_66980 69627 55.629 36.74 0.988 0.982 0.977
k141_18481 69550 55.449 37.68 0.988 0.979 0.973
k141_12507 69444 43.459 14.16 0.991 0.988 0.984
k141_53754 69252 42.241 123.00 0.987 0.983 0.980
k141_48961 69195 44.114 20.24 0.990 0.987 0.984
k141_91109 68840 45.521 50.88 0.993 0.990 0.986
k141_11056 68757 41.164 21.55 0.983 0.978 0.975
k141_26472 68746 44.839 93.03 0.991 0.987 0.983
k141_37115 68739 47.573 74.20 0.995 0.992 0.988
k141_84163 68686 59.672 13.53 0.979 0.975 0.970
k141_53595 68586 43.108 11.02 0.990 0.986 0.983
k141_90143 68551 56.476 15.75 0.991 0.987 0.982
k141_85481 68049 52.543 22.40 0.993 0.990 0.986
k141_31707 68038 63.279 16.26 0.967 0.963 0.958
k141_5417 67970 55.463 14.29 0.991 0.988 0.983
k141_80671 67417 44.078 22.32 0.990 0.985 0.981
k141_83034 67042 52.695 24.61 0.996 0.992 0.988
k141_12520 66824 53.011 22.45 0.992 0.989 0.985
k141_12900 66786 46.450 12.08 0.993 0.991 0.988
k141_99795 66704 43.903 119.34 0.991 0.988 0.985
k141_89974 66463 51.358 13.53 0.996 0.991 0.987
k141_36054 65879 42.655 96.62 0.988 0.985 0.981
k141_45392 65839 40.614 15.61 0.982 0.978 0.976
k141_88676 65573 56.209 39.65 0.986 0.977 0.970
k141_44203 65540 44.173 13.94 0.991 0.988 0.985
k141_73828 65169 43.871 17.64 0.992 0.988 0.985
k141_87560 65103 44.376 99.09 0.992 0.988 0.985
k141_100169 64722 42.024 13.03 0.989 0.986 0.984
k141_78129 64415 52.750 11.50 0.992 0.988 0.984
k141_89605 64269 54.319 16.13 0.993 0.989 0.985
k141_90473 63846 47.496 53.99 0.995 0.992 0.988
k141_99599 63740 53.106 12.14 0.992 0.989 0.985
k141_58602 63526 50.530 25.35 0.995 0.992 0.988
k141_1777 63424 56.644 39.72 0.982 0.973 0.966
k141_45527 62988 44.342 15.48 0.991 0.987 0.984
k141_8214 62799 42.962 96.00 0.990 0.988 0.985
k141_25478 62664 43.993 42.53 0.990 0.987 0.984
k141_32394 62354 44.894 12.88 0.993 0.989 0.985
k141_50382 61911 43.231 110.41 0.988 0.985 0.982
k141_853 61831 46.643 102.31 0.994 0.991 0.987
k141_8318 61517 42.123 88.51 0.988 0.985 0.982
k141_25670 61337 56.460 35.08 0.986 0.978 0.972
k141_88172 61296 55.284 33.94 0.987 0.978 0.972
k141_34243 60876 52.181 24.33 0.994 0.989 0.985
k141_22312 60876 52.592 13.68 0.995 0.990 0.986
k141_38352 60443 56.581 34.53 0.986 0.978 0.972
k141_46244 60181 53.038 12.67 0.992 0.988 0.984
k141_16326 60064 58.724 14.14 0.983 0.978 0.973
k141_26981 59990 43.179 23.63 0.989 0.985 0.981
k141_1132 59800 45.682 14.45 0.995 0.990 0.986
k141_63871 59776 43.967 16.20 0.991 0.987 0.983
k141_27265 59458 46.885 30.00 0.995 0.993 0.989
k141_29165 59372 55.351 14.66 0.991 0.987 0.983
k141_13887 59208 42.369 21.43 0.987 0.984 0.980
k141_3602 59040 44.245 14.19 0.993 0.989 0.985
k141_66406 58995 39.717 12.92 0.981 0.977 0.974
k141_93601 58841 43.057 23.53 0.989 0.984 0.981
k141_65872 58656 49.441 75.64 0.995 0.992 0.988
k141_16044 58605 44.111 11.81 0.990 0.986 0.982
k141_94901 58591 46.157 18.04 0.992 0.987 0.983
k141_64624 58509 42.349 169.23 0.985 0.980 0.974
k141_93171 58491 47.691 46.68 0.995 0.992 0.987
k141_77244 58481 57.391 45.25 0.984 0.975 0.968
k141_25671 58239 47.501 16.59 0.993 0.990 0.986
k141_90543 58051 52.056 20.77 0.990 0.986 0.981
k141_47239 58003 53.904 14.39 0.990 0.985 0.981
k141_81588 57948 53.117 21.82 0.991 0.987 0.983
k141_80345 57717 59.624 14.82 0.978 0.973 0.968
k141_89998 57700 42.738 117.05 0.988 0.985 0.982
k141_7913 57599 64.234 12.71 0.963 0.958 0.953
k141_29415 56919 56.514 39.31 0.984 0.976 0.969
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k141_80451 3056 45.746 5.06 0.987 0.975 0.964
k141_73020 3056 38.973 7.72 0.968 0.964 0.957
k141_67919 3056 54.777 11.81 0.986 0.978 0.968
k141_92735 3055 46.972 5.62 0.993 0.984 0.974
k141_64422 3055 49.034 8.86 0.994 0.989 0.980
k141_48718 3055 41.538 5.90 0.970 0.962 0.952
k141_62766 3054 50.524 6.24 0.993 0.982 0.970
k141_72817 3052 41.776 4.75 0.956 0.948 0.935
k141_42385 3052 54.161 20.01 0.989 0.981 0.971
k141_37102 3050 46.918 47.35 0.991 0.985 0.974
k141_36912 3049 32.765 6.23 0.953 0.950 0.944
k141_14767 3049 59.134 6.02 0.983 0.973 0.960
k141_41577 3048 47.605 5.25 0.991 0.986 0.976
k141_23540 3048 33.924 7.98 0.938 0.925 0.910
k141_98739 3047 29.865 5.83 0.928 0.919 0.908
k141_2635 3047 52.872 6.64 0.994 0.985 0.973
k141_69472 3046 47.045 5.77 0.988 0.977 0.966
k141_23074 3045 51.067 6.31 0.982 0.971 0.959
k141_15427 3045 45.025 7.58 0.990 0.984 0.975
k141_22099 3044 55.618 5.27 0.970 0.963 0.949
k141_28855 3043 52.613 201.00 0.989 0.982 0.972
k141_13476 3043 45.613 7.45 0.990 0.984 0.975
k141_75758 3042 53.123 175.85 0.994 0.989 0.980
k141_14341 3042 58.481 6.07 0.972 0.959 0.944
k141_1390 3041 48.767 6.26 0.983 0.971 0.960
Displaying first 5000 of 98769 lines

Table 3 . Basic contig statistics, filtered contigs

Table 3 . Basic contig statistics, filtered contigs
#ID length GC coverage S_2mer S_3mer S_4mer
k141_76796 386035 51.891 25.07 0.995 0.992 0.988
k141_76622 349967 48.068 72.00 0.995 0.992 0.988
k141_59322 343211 43.778 14.94 0.991 0.987 0.984
k141_20763 342700 47.468 52.60 0.995 0.992 0.988
k141_39922 334399 46.144 42.71 0.995 0.992 0.988
k141_13684 299237 42.963 103.09 0.989 0.986 0.983
k141_73726 298100 46.238 47.36 0.994 0.991 0.987
k141_24136 297381 42.899 23.73 0.989 0.985 0.982
k141_58801 294754 53.046 25.60 0.995 0.991 0.988
k141_79554 290324 43.567 16.03 0.992 0.988 0.985
k141_68893 284177 59.188 15.97 0.980 0.976 0.971
k141_62980 260876 51.135 89.77 0.996 0.993 0.990
k141_75130 260643 47.584 52.43 0.996 0.992 0.989
k141_80951 254966 56.175 45.04 0.985 0.977 0.971
k141_73145 254313 51.474 85.52 0.996 0.993 0.989
k141_72709 250564 60.446 19.20 0.977 0.972 0.968
k141_98708 248942 44.240 18.88 0.991 0.986 0.982
k141_78261 247234 49.724 83.68 0.996 0.993 0.990
k141_43615 244785 45.550 45.20 0.993 0.990 0.986
k141_80866 237758 59.367 18.56 0.980 0.976 0.971
k141_28842 231031 43.386 20.94 0.990 0.986 0.983
k141_87164 215400 45.105 48.78 0.991 0.988 0.984
k141_53271 212672 45.597 23.30 0.994 0.989 0.985
k141_68261 212292 42.070 14.27 0.988 0.985 0.982
k141_1964 206776 52.656 24.67 0.994 0.990 0.986
k141_33256 201433 47.380 53.63 0.996 0.992 0.989
k141_48872 197638 42.358 15.11 0.988 0.985 0.982
k141_81796 187223 52.728 25.26 0.995 0.990 0.987
k141_88700 185271 47.826 44.11 0.996 0.993 0.989
k141_47485 183654 59.867 20.51 0.979 0.974 0.970
k141_25275 181053 42.652 23.44 0.988 0.984 0.981
k141_26205 181034 55.255 37.47 0.987 0.979 0.973
k141_16269 179431 59.056 18.56 0.980 0.975 0.970
k141_33897 176101 52.275 24.71 0.994 0.990 0.986
k141_45061 175853 45.180 13.81 0.995 0.991 0.987
k141_94778 174227 44.188 16.42 0.991 0.987 0.983
k141_62931 171919 52.663 64.41 0.996 0.993 0.989
k141_88682 171703 44.804 107.51 0.992 0.989 0.986
k141_95298 170655 55.980 15.15 0.991 0.986 0.982
k141_36328 169718 56.888 15.60 0.989 0.985 0.980
k141_55934 167063 43.125 96.18 0.988 0.985 0.981
k141_76713 166254 54.122 14.82 0.994 0.991 0.987
k141_40528 165793 43.850 24.57 0.990 0.985 0.982
k141_91618 164123 44.333 20.74 0.991 0.986 0.983
k141_79956 163397 43.819 15.31 0.991 0.987 0.984
k141_43116 159435 46.194 57.12 0.994 0.990 0.987
k141_10798 158681 42.615 17.25 0.988 0.984 0.981
k141_80995 158391 55.843 15.87 0.988 0.984 0.980
k141_64596 158147 42.932 89.73 0.989 0.986 0.983
k141_22008 157936 51.005 20.90 0.994 0.991 0.987
k141_33425 156971 59.596 14.22 0.979 0.974 0.970
k141_91139 155923 43.156 23.92 0.989 0.985 0.981
k141_52536 154384 46.888 51.21 0.995 0.992 0.988
k141_3747 153662 43.432 90.19 0.990 0.987 0.984
k141_89123 153142 42.690 15.95 0.989 0.984 0.981
k141_32389 152608 42.628 116.30 0.989 0.986 0.983
k141_81018 151527 43.632 15.40 0.990 0.987 0.983
k141_46214 151251 45.147 15.63 0.993 0.989 0.985
k141_27196 150924 44.891 19.95 0.993 0.989 0.985
k141_96249 149946 59.731 15.54 0.979 0.974 0.969
k141_13535 149705 42.163 102.94 0.988 0.985 0.981
k141_44155 149580 47.624 11.12 0.996 0.993 0.990
k141_8629 148489 44.524 16.69 0.992 0.987 0.983
k141_12912 146001 43.604 108.46 0.990 0.987 0.983
k141_72815 142594 51.029 83.03 0.995 0.992 0.988
k141_90142 142375 41.815 106.18 0.987 0.984 0.981
k141_99945 142287 43.939 98.42 0.991 0.988 0.984
k141_49643 141763 42.184 105.34 0.987 0.984 0.980
k141_55038 136465 56.687 40.01 0.985 0.976 0.970
k141_21442 135421 59.063 17.79 0.979 0.974 0.970
k141_50679 135091 56.597 17.39 0.987 0.982 0.979
k141_26230 133070 49.678 75.87 0.995 0.992 0.988
k141_84469 132617 45.896 22.88 0.993 0.990 0.987
k141_94955 132166 43.645 105.87 0.989 0.986 0.982
k141_14515 131559 55.999 43.27 0.985 0.977 0.971
k141_34789 130310 40.701 93.21 0.984 0.981 0.978
k141_57446 130099 43.392 22.82 0.990 0.986 0.982
k141_45960 128383 41.062 21.23 0.984 0.980 0.977
k141_83366 128221 50.585 18.63 0.993 0.989 0.985
k141_26590 123846 43.918 17.98 0.992 0.988 0.985
k141_90685 123773 51.598 20.23 0.992 0.988 0.984
k141_51840 122240 58.581 15.35 0.981 0.977 0.972
k141_57014 122187 49.666 17.62 0.992 0.988 0.984
k141_64137 122120 50.239 19.07 0.994 0.990 0.986
k141_54940 121746 50.261 28.21 0.996 0.992 0.989
k141_10440 121245 49.061 87.41 0.997 0.994 0.990
k141_43267 121088 47.904 50.64 0.996 0.993 0.989
k141_63716 119854 56.956 15.11 0.987 0.984 0.979
k141_57996 119376 41.513 21.66 0.985 0.982 0.978
k141_61276 119182 46.954 21.98 0.995 0.992 0.989
k141_51931 116680 49.949 18.01 0.995 0.992 0.989
k141_2796 115178 42.488 18.42 0.988 0.985 0.981
k141_23606 113096 52.944 86.49 0.995 0.992 0.987
k141_51271 112899 50.967 82.20 0.995 0.992 0.987
k141_26306 111928 42.784 21.72 0.988 0.983 0.980
k141_84762 111178 57.975 13.71 0.985 0.981 0.977
k141_57522 110986 58.216 13.72 0.983 0.978 0.974
k141_53107 110719 42.642 23.87 0.989 0.985 0.982
k141_58297 109773 47.991 22.63 0.995 0.991 0.988
k141_86083 108052 47.107 50.64 0.995 0.991 0.987
k141_52132 106536 43.321 114.56 0.990 0.986 0.983
k141_5185 105585 53.258 26.79 0.996 0.992 0.988
k141_3574 105582 42.673 93.96 0.988 0.985 0.982
k141_83948 105003 53.173 25.65 0.995 0.991 0.987
k141_24846 104179 48.006 49.06 0.996 0.992 0.987
k141_37907 104038 51.698 87.98 0.997 0.994 0.990
k141_59966 103194 43.400 23.45 0.991 0.987 0.985
k141_96042 103058 42.712 21.32 0.988 0.983 0.980
k141_16623 102470 49.339 77.13 0.996 0.993 0.989
k141_95816 102259 44.355 12.87 0.993 0.989 0.985
k141_28755 102246 51.984 24.47 0.996 0.992 0.989
k141_71278 100834 46.324 47.85 0.993 0.989 0.985
k141_81778 100649 44.045 18.49 0.991 0.986 0.983
k141_43117 100016 42.449 110.34 0.987 0.984 0.981
k141_55162 98611 41.167 19.11 0.986 0.982 0.979
k141_38172 98169 49.635 22.80 0.996 0.993 0.990
k141_85651 96475 43.396 22.21 0.990 0.986 0.982
k141_82090 96469 51.144 19.36 0.993 0.990 0.986
k141_13630 95969 54.683 35.04 0.988 0.977 0.971
k141_8569 95611 42.234 20.44 0.986 0.982 0.979
k141_15332 93673 44.244 15.51 0.993 0.989 0.985
k141_46494 92386 54.760 31.56 0.992 0.987 0.983
k141_79209 92097 46.030 44.32 0.993 0.990 0.986
k141_66732 91819 51.926 25.14 0.994 0.990 0.986
k141_21079 91155 45.924 48.95 0.992 0.988 0.984
k141_13736 90938 44.397 17.12 0.990 0.985 0.981
k141_5631 90881 48.547 94.18 0.996 0.993 0.989
k141_90296 90510 56.724 41.99 0.983 0.974 0.968
k141_34098 90004 56.279 15.73 0.990 0.985 0.981
k141_35975 89692 53.772 12.20 0.994 0.991 0.988
k141_75747 89188 44.223 91.06 0.991 0.988 0.985
k141_28050 88763 57.652 15.81 0.988 0.984 0.979
k141_86549 87926 42.047 111.42 0.987 0.984 0.981
k141_7211 87173 44.237 14.96 0.992 0.988 0.984
k141_80346 86661 43.464 16.92 0.990 0.985 0.982
k141_97217 86515 53.057 23.08 0.993 0.989 0.985
k141_62364 86454 41.184 12.43 0.986 0.982 0.979
k141_3009 86200 51.450 75.95 0.995 0.992 0.987
k141_1311 85903 39.908 19.07 0.980 0.977 0.974
k141_92677 85546 54.970 15.51 0.988 0.983 0.979
k141_56954 85296 55.862 37.52 0.985 0.977 0.970
k141_22015 85138 45.083 111.37 0.992 0.988 0.985
k141_58624 84646 51.024 24.24 0.996 0.992 0.988
k141_77146 84614 35.912 49.67 0.963 0.958 0.954
k141_86556 84206 41.352 91.45 0.986 0.984 0.980
k141_31425 83402 43.096 21.66 0.989 0.984 0.979
k141_66283 82819 55.090 34.98 0.991 0.987 0.984
k141_25112 81974 55.830 35.09 0.986 0.977 0.971
k141_97367 81821 42.415 22.69 0.987 0.983 0.979
k141_5988 81384 64.865 13.76 0.961 0.957 0.951
k141_91557 81285 53.550 22.98 0.992 0.989 0.984
k141_78591 80719 42.082 14.31 0.988 0.984 0.981
k141_42611 80405 58.367 15.37 0.981 0.976 0.971
k141_62001 80387 56.054 14.72 0.986 0.982 0.978
k141_3781 80201 40.598 17.89 0.983 0.980 0.977
k141_89298 80066 41.920 96.50 0.986 0.983 0.980
k141_97371 78463 45.776 18.98 0.994 0.989 0.984
k141_92065 78312 43.320 12.16 0.990 0.987 0.983
k141_51270 78264 46.719 51.41 0.995 0.991 0.987
k141_85999 78120 41.852 22.72 0.986 0.981 0.978
k141_81552 77910 45.975 15.88 0.993 0.989 0.986
k141_55796 77458 44.700 18.74 0.993 0.989 0.985
k141_25568 76803 49.157 44.88 0.996 0.993 0.988
k141_19045 76740 40.658 11.52 0.984 0.981 0.979
k141_89380 76728 57.822 42.99 0.983 0.974 0.968
k141_31275 76690 43.735 15.42 0.990 0.987 0.983
k141_90547 76151 55.318 44.24 0.987 0.978 0.972
k141_3603 75988 42.984 111.38 0.989 0.986 0.983
k141_55711 75523 56.493 37.27 0.986 0.976 0.970
k141_50966 75121 47.984 22.56 0.995 0.992 0.989
k141_68058 75099 53.561 14.01 0.993 0.988 0.984
k141_63582 74975 49.931 96.86 0.997 0.995 0.991
k141_26510 74893 56.276 43.54 0.983 0.974 0.968
k141_27478 74523 43.733 15.15 0.991 0.987 0.983
k141_81741 74238 41.145 24.37 0.984 0.979 0.976
k141_55665 74036 46.715 15.86 0.996 0.992 0.989
k141_26190 72914 54.493 49.21 0.990 0.981 0.975
k141_9227 71637 43.108 112.71 0.990 0.987 0.984
k141_46279 71566 59.732 17.40 0.979 0.974 0.968
k141_2573 71295 44.127 18.10 0.989 0.983 0.979
k141_31680 69867 54.332 41.53 0.988 0.981 0.975
k141_66980 69627 55.629 37.32 0.988 0.982 0.977
k141_18481 69550 55.449 37.73 0.988 0.979 0.973
k141_12507 69444 43.459 14.18 0.991 0.988 0.984
k141_53754 69252 42.241 123.54 0.987 0.983 0.980
k141_48961 69195 44.114 25.66 0.990 0.987 0.984
k141_91109 68840 45.521 50.89 0.993 0.990 0.986
k141_11056 68757 41.164 21.59 0.983 0.978 0.975
k141_26472 68746 44.839 94.06 0.991 0.987 0.983
k141_37115 68739 47.573 74.24 0.995 0.992 0.988
k141_84163 68686 59.672 14.27 0.979 0.975 0.970
k141_53595 68586 43.108 11.12 0.990 0.986 0.983
k141_90143 68551 56.476 15.79 0.991 0.987 0.982
k141_85481 68049 52.543 22.47 0.993 0.990 0.986
k141_31707 68038 63.279 16.32 0.967 0.963 0.958
k141_5417 67970 55.463 14.33 0.991 0.988 0.983
k141_80671 67417 44.078 22.38 0.990 0.985 0.981
k141_83034 67042 52.695 24.78 0.996 0.992 0.988
k141_12520 66824 53.011 22.49 0.992 0.989 0.985
k141_12900 66786 46.450 12.16 0.993 0.991 0.988
k141_99795 66704 43.903 119.35 0.991 0.988 0.985
k141_89974 66463 51.358 13.58 0.996 0.991 0.987
k141_36054 65879 42.655 96.65 0.988 0.985 0.981
k141_45392 65839 40.614 15.64 0.982 0.978 0.976
k141_88676 65573 56.209 39.70 0.986 0.977 0.970
k141_44203 65540 44.173 13.96 0.991 0.988 0.985
k141_73828 65169 43.871 20.78 0.992 0.988 0.985
k141_87560 65103 44.376 99.11 0.992 0.988 0.985
k141_100169 64722 42.024 13.05 0.989 0.986 0.984
k141_78129 64415 52.750 11.88 0.992 0.988 0.984
k141_89605 64269 54.319 16.17 0.993 0.989 0.985
k141_90473 63846 47.496 54.00 0.995 0.992 0.988
k141_99599 63740 53.106 12.17 0.992 0.989 0.985
k141_58602 63526 50.530 25.38 0.995 0.992 0.988
k141_1777 63424 56.644 41.35 0.982 0.973 0.966
k141_45527 62988 44.342 15.48 0.991 0.987 0.984
k141_8214 62799 42.962 98.87 0.990 0.988 0.985
k141_25478 62664 43.993 42.57 0.990 0.987 0.984
k141_32394 62354 44.894 12.92 0.993 0.989 0.985
k141_50382 61911 43.231 110.56 0.988 0.985 0.982
k141_853 61831 46.643 102.32 0.994 0.991 0.987
k141_8318 61517 42.123 96.79 0.988 0.985 0.982
k141_25670 61337 56.460 35.86 0.986 0.978 0.972
k141_88172 61296 55.284 34.64 0.987 0.978 0.972
k141_34243 60876 52.181 24.37 0.994 0.989 0.985
k141_22312 60876 52.592 13.70 0.995 0.990 0.986
k141_38352 60443 56.581 35.34 0.986 0.978 0.972
k141_46244 60181 53.038 12.77 0.992 0.988 0.984
k141_16326 60064 58.724 14.25 0.983 0.978 0.973
k141_26981 59990 43.179 23.80 0.989 0.985 0.981
k141_1132 59800 45.682 14.46 0.995 0.990 0.986
k141_63871 59776 43.967 16.21 0.991 0.987 0.983
k141_27265 59458 46.885 30.34 0.995 0.993 0.989
k141_29165 59372 55.351 14.76 0.991 0.987 0.983
k141_13887 59208 42.369 21.45 0.987 0.984 0.980
k141_3602 59040 44.245 14.22 0.993 0.989 0.985
k141_66406 58995 39.717 12.96 0.981 0.977 0.974
k141_93601 58841 43.057 26.69 0.989 0.984 0.981
k141_65872 58656 49.441 76.47 0.995 0.992 0.988
k141_16044 58605 44.111 11.84 0.990 0.986 0.982
k141_94901 58591 46.157 18.08 0.992 0.987 0.983
k141_64624 58509 42.349 169.28 0.985 0.980 0.974
k141_93171 58491 47.691 46.70 0.995 0.992 0.987
k141_77244 58481 57.391 46.54 0.984 0.975 0.968
k141_25671 58239 47.501 16.60 0.993 0.990 0.986
k141_90543 58051 52.056 20.82 0.990 0.986 0.981
k141_47239 58003 53.904 14.43 0.990 0.985 0.981
k141_81588 57948 53.117 21.86 0.991 0.987 0.983
k141_80345 57717 59.624 14.88 0.978 0.973 0.968
k141_89998 57700 42.738 118.50 0.988 0.985 0.982
k141_7913 57599 64.234 12.76 0.963 0.958 0.953
k141_29415 56919 56.514 40.08 0.984 0.976 0.969
k141_64391 56866 60.894 17.28 0.976 0.972 0.968
k141_37161 56716 41.692 15.54 0.985 0.981 0.978
k141_30066 56616 56.044 36.64 0.986 0.977 0.971
k141_89764 56557 51.842 22.65 0.996 0.992 0.988
k141_54064 56533 60.324 14.89 0.976 0.970 0.964
k141_90850 55816 44.729 17.51 0.992 0.988 0.984
k141_85070 55733 49.606 36.98 0.995 0.989 0.985
k141_67837 55635 59.155 16.11 0.980 0.975 0.970
k141_74994 55279 55.234 37.40 0.985 0.974 0.966
k141_39069 55114 55.944 14.07 0.990 0.985 0.981
k141_52445 55094 47.276 20.66 0.995 0.990 0.986
k141_94273 54927 48.625 54.71 0.996 0.991 0.987
k141_7487 54718 44.742 22.20 0.991 0.987 0.983
k141_17337 54341 49.515 32.72 0.986 0.980 0.973
k141_15966 53913 41.344 112.62 0.986 0.983 0.980
k141_52703 53846 46.167 49.38 0.994 0.990 0.986
k141_45694 53749 44.717 51.68 0.992 0.988 0.984
k141_40988 53036 62.220 16.69 0.971 0.967 0.962
k141_48083 52537 58.277 15.50 0.986 0.982 0.977
k141_39651 52015 47.075 17.66 0.995 0.990 0.986
k141_20220 51872 58.457 14.43 0.982 0.978 0.973
k141_94728 51794 55.829 13.35 0.990 0.986 0.982
k141_91238 51625 44.922 11.53 0.993 0.989 0.985
k141_68634 51602 43.386 13.93 0.989 0.985 0.982
k141_83733 51555 45.988 47.03 0.994 0.990 0.986
k141_50505 51329 43.506 117.80 0.990 0.986 0.983
k141_63054 50981 52.280 22.70 0.991 0.988 0.984
k141_6613 50824 48.105 12.25 0.990 0.985 0.981
k141_50448 50459 53.087 23.88 0.995 0.990 0.986
k141_42948 50057 41.327 93.29 0.984 0.982 0.978
k141_74605 49972 44.275 10.86 0.992 0.988 0.984
k141_73783 49828 56.819 38.75 0.983 0.976 0.971
k141_75837 49751 43.601 15.35 0.990 0.985 0.981
k141_58818 49587 52.869 12.71 0.995 0.991 0.987
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k141_45369 3220 56.398 308.60 0.989 0.983 0.973
k141_44835 3220 42.298 15.15 0.985 0.979 0.970
k141_37121 3219 53.464 6.46 0.991 0.982 0.970
k141_6980 3218 53.045 13.00 0.986 0.979 0.968
k141_64558 3218 38.844 8.81 0.966 0.961 0.952
k141_61869 3218 42.573 6.09 0.989 0.985 0.977
k141_35937 3218 57.924 5.81 0.982 0.973 0.962
k141_100171 3218 51.181 6.58 0.993 0.986 0.977
k141_38831 3217 52.782 7.28 0.991 0.984 0.974
k141_33960 3217 49.549 23.69 0.995 0.988 0.980
k141_2951 3217 37.644 7.75 0.972 0.968 0.958
k141_94983 3216 51.897 7.69 0.984 0.975 0.961
k141_26093 3216 54.664 5.60 0.990 0.980 0.967
k141_91431 3215 47.465 197.64 0.995 0.989 0.980
k141_38739 3215 58.414 4.89 0.976 0.965 0.951
k141_26308 3215 44.572 20.18 0.986 0.979 0.969
k141_67991 3214 44.493 7.30 0.988 0.983 0.975
k141_20373 3213 39.527 7.21 0.979 0.974 0.965
k141_70868 3212 57.472 6.81 0.986 0.976 0.964
k141_5161 3212 43.555 6.43 0.985 0.975 0.966
k141_55778 3211 56.400 7.08 0.988 0.978 0.966
k141_25306 3210 54.237 10.78 0.990 0.982 0.972
k141_71267 3209 36.740 6.76 0.968 0.964 0.957
k141_23724 3209 48.800 7.23 0.978 0.968 0.957
k141_47062 3208 41.615 137.61 0.969 0.956 0.944
k141_90659 3207 56.657 8.03 0.983 0.972 0.960
k141_88185 3207 41.815 71.49 0.986 0.980 0.972
k141_7446 3207 55.722 27.99 0.989 0.977 0.963
k141_89423 3206 52.308 5.16 0.988 0.977 0.966
k141_88532 3206 44.635 7.32 0.992 0.987 0.979
k141_29564 3206 45.945 7.74 0.973 0.962 0.950
k141_58926 3205 57.660 6.24 0.987 0.979 0.969
k141_17433 3204 47.222 6.75 0.994 0.991 0.983
k141_23313 3203 54.667 6.99 0.985 0.975 0.963
k141_38010 3202 47.626 11.98 0.993 0.988 0.978
k141_36397 3202 51.187 5.35 0.993 0.984 0.975
k141_85372 3201 51.515 33.57 0.993 0.981 0.970
k141_12385 3201 54.608 6.31 0.991 0.984 0.974
k141_71298 3200 43.344 5.57 0.986 0.979 0.969
k141_74220 3199 32.698 5.15 0.937 0.931 0.923
k141_61106 3199 43.170 8.27 0.988 0.985 0.977
k141_27087 3199 43.889 15.24 0.985 0.975 0.962
k141_22958 3199 42.451 8.55 0.985 0.981 0.975
k141_97580 3198 44.028 7.05 0.987 0.982 0.973
k141_91801 3198 45.841 6.65 0.982 0.977 0.968
k141_70879 3198 48.624 7.78 0.993 0.989 0.981
k141_32839 3198 49.625 54.52 0.989 0.983 0.971
k141_59257 3197 53.550 6.42 0.991 0.985 0.975
k141_37369 3197 52.831 6.64 0.994 0.987 0.978
k141_71069 3196 54.850 5.43 0.979 0.969 0.956
k141_4856 3196 41.771 12.44 0.985 0.980 0.971
k141_97418 3195 53.521 9.34 0.978 0.965 0.954
k141_92574 3195 46.792 10.35 0.986 0.977 0.967
k141_32823 3195 49.077 12.86 0.990 0.984 0.972
k141_15334 3195 52.582 5.34 0.992 0.983 0.973
k141_90262 3192 54.292 10.34 0.991 0.981 0.965
k141_66492 3192 47.243 8.92 0.987 0.978 0.968
k141_1336 3191 48.543 9.00 0.994 0.988 0.979
k141_84257 3190 48.025 36.76 0.991 0.986 0.977
k141_83204 3190 36.583 7.90 0.969 0.966 0.960
k141_49713 3189 53.998 8.08 0.993 0.985 0.975
k141_39491 3189 53.402 6.83 0.992 0.984 0.974
k141_79418 3188 50.376 6.23 0.991 0.982 0.971
k141_61541 3188 50.659 9.09 0.993 0.989 0.980
k141_68826 3187 52.024 8.13 0.992 0.988 0.979
k141_91225 3186 55.838 217.85 0.986 0.977 0.966
k141_74044 3186 55.681 11.05 0.989 0.983 0.975
k141_20103 3185 53.375 5.90 0.989 0.982 0.971
k141_91614 3184 48.964 10.43 0.986 0.974 0.962
k141_75605 3184 56.407 5.38 0.975 0.963 0.950
k141_38279 3184 29.083 6.93 0.928 0.921 0.911
k141_6572 3183 50.927 8.81 0.996 0.988 0.976
k141_84341 3181 44.231 6.64 0.991 0.988 0.980
k141_71835 3181 53.568 7.42 0.979 0.967 0.955
k141_42178 3181 50.047 7.21 0.992 0.981 0.971
k141_27575 3181 46.558 15.01 0.981 0.971 0.959
k141_75246 3180 59.403 7.65 0.974 0.961 0.947
k141_98272 3179 42.812 7.28 0.975 0.965 0.953
k141_62258 3179 33.501 4.97 0.946 0.938 0.927
k141_68656 3174 51.103 6.03 0.987 0.976 0.963
k141_48801 3174 38.217 6.71 0.969 0.963 0.955
k141_74074 3173 57.390 7.02 0.980 0.965 0.948
k141_98181 3172 28.846 6.51 0.917 0.905 0.893
k141_27573 3170 46.246 8.39 0.994 0.990 0.983
k141_98313 3169 39.066 7.27 0.970 0.965 0.958
k141_92653 3168 40.278 6.63 0.980 0.975 0.967
k141_67151 3168 62.973 8.98 0.962 0.955 0.944
k141_25333 3168 47.980 78.09 0.990 0.987 0.981
k141_62668 3167 34.828 16.77 0.959 0.951 0.939
k141_48773 3167 64.888 6.41 0.960 0.953 0.943
k141_84435 3166 53.348 7.50 0.986 0.978 0.966
k141_83709 3166 40.082 8.97 0.973 0.967 0.960
k141_67774 3166 57.517 7.19 0.983 0.974 0.961
k141_61533 3166 37.018 8.02 0.966 0.962 0.955
k141_53246 3166 49.842 6.76 0.994 0.988 0.979
k141_50051 3166 50.063 54.80 0.993 0.987 0.976
k141_48223 3166 36.260 8.17 0.966 0.959 0.950
k141_87116 3165 54.439 6.22 0.990 0.982 0.971
k141_37196 3165 41.264 8.42 0.982 0.978 0.969
k141_50475 3164 37.579 12.20 0.972 0.968 0.958
k141_465 3164 33.502 5.71 0.941 0.928 0.912
k141_40657 3164 59.893 5.97 0.979 0.969 0.956
k141_7505 3161 49.668 8.88 0.986 0.975 0.963
k141_61273 3160 41.646 5.92 0.968 0.959 0.947
k141_27773 3160 52.184 5.78 0.994 0.987 0.978
k141_89548 3157 53.595 10.70 0.993 0.988 0.978
k141_85363 3157 56.351 7.62 0.988 0.978 0.965
k141_90065 3156 48.606 14.57 0.991 0.985 0.976
k141_44660 3156 41.381 8.02 0.982 0.977 0.968
k141_86154 3153 50.682 9.50 0.991 0.977 0.966
k141_53967 3152 58.312 7.89 0.952 0.940 0.926
k141_32946 3152 39.150 7.04 0.974 0.970 0.963
k141_79100 3150 45.175 7.52 0.985 0.978 0.968
k141_4883 3150 48.667 10.90 0.986 0.976 0.962
k141_94467 3149 57.637 6.77 0.983 0.973 0.962
k141_91924 3149 53.700 10.52 0.992 0.985 0.973
k141_611 3148 50.032 7.25 0.985 0.975 0.966
k141_24914 3148 48.666 5.37 0.992 0.986 0.977
k141_46412 3146 48.093 6.92 0.993 0.988 0.978
k141_67767 3144 46.501 8.45 0.990 0.982 0.971
k141_67581 3144 49.809 6.14 0.992 0.984 0.975
k141_72736 3143 40.757 5.99 0.969 0.963 0.955
k141_39622 3143 50.970 8.16 0.970 0.959 0.946
k141_93855 3142 34.087 5.20 0.958 0.954 0.945
k141_19778 3142 38.701 6.25 0.978 0.974 0.968
k141_41257 3140 48.280 6.01 0.993 0.981 0.969
k141_96485 3138 52.199 21.67 0.986 0.980 0.970
k141_21287 3138 37.412 7.13 0.971 0.968 0.962
k141_18781 3138 50.319 11.32 0.995 0.991 0.983
k141_98416 3137 33.918 6.51 0.946 0.935 0.921
k141_61162 3136 41.295 8.32 0.979 0.974 0.965
k141_44908 3136 38.616 9.70 0.973 0.969 0.960
k141_86669 3135 37.448 6.62 0.968 0.963 0.956
k141_34701 3135 46.061 10.57 0.990 0.981 0.972
k141_29633 3135 48.644 5.09 0.993 0.985 0.975
k141_58564 3134 42.597 6.81 0.988 0.983 0.974
k141_35814 3134 33.312 7.94 0.948 0.943 0.930
k141_97564 3133 50.686 12.68 0.996 0.991 0.981
k141_2759 3133 55.027 9.32 0.972 0.958 0.945
k141_63048 3130 51.693 7.92 0.987 0.979 0.970
k141_92155 3129 47.299 6.84 0.992 0.982 0.971
k141_54932 3129 50.815 13.64 0.990 0.979 0.967
k141_61781 3127 52.638 5.15 0.995 0.987 0.976
k141_58238 3125 51.584 5.73 0.994 0.985 0.976
k141_94108 3123 43.612 8.02 0.985 0.977 0.968
k141_90673 3123 48.415 6.04 0.982 0.972 0.956
k141_18120 3123 47.711 89.93 0.995 0.992 0.985
k141_87230 3122 53.812 27.59 0.985 0.975 0.962
k141_20510 3122 52.819 8.25 0.981 0.969 0.958
k141_70391 3121 32.426 5.80 0.940 0.931 0.919
k141_9045 3119 41.969 6.25 0.987 0.982 0.974
k141_36346 3119 43.988 15.77 0.989 0.984 0.977
k141_12514 3118 33.900 7.77 0.940 0.936 0.928
k141_95076 3117 55.951 7.50 0.984 0.974 0.963
k141_90115 3116 53.049 10.40 0.991 0.985 0.976
k141_13724 3116 50.513 10.64 0.994 0.987 0.976
k141_62620 3115 50.883 7.71 0.996 0.989 0.979
k141_685 3114 51.541 7.08 0.993 0.983 0.971
k141_31802 3114 30.379 5.64 0.928 0.915 0.901
k141_66648 3113 52.168 9.96 0.991 0.986 0.976
k141_7906 3112 43.766 20.99 0.986 0.976 0.965
k141_86893 3111 42.269 8.97 0.986 0.979 0.970
k141_40999 3111 40.791 8.82 0.983 0.979 0.971
k141_28540 3111 37.287 6.02 0.961 0.954 0.944
k141_77083 3110 54.952 6.37 0.985 0.975 0.964
k141_34086 3110 40.611 8.05 0.981 0.977 0.969
k141_22876 3110 63.215 11.73 0.962 0.952 0.939
k141_47607 3109 46.156 9.69 0.990 0.981 0.971
k141_60314 3108 53.057 8.14 0.995 0.990 0.982
k141_42960 3108 47.426 6.34 0.988 0.983 0.974
k141_97298 3107 47.473 8.00 0.978 0.968 0.957
k141_51133 3107 55.391 5.09 0.984 0.976 0.965
k141_65672 3106 52.737 5.45 0.990 0.980 0.970
k141_34955 3105 40.676 12.05 0.974 0.969 0.960
k141_82844 3104 35.599 6.38 0.957 0.948 0.938
k141_67456 3103 53.432 16.14 0.985 0.977 0.968
k141_20535 3103 25.814 15.94 0.904 0.901 0.894
k141_60158 3102 49.001 14.03 0.991 0.986 0.976
k141_21999 3102 52.257 6.36 0.990 0.980 0.968
k141_77456 3101 37.955 7.91 0.958 0.953 0.945
k141_56783 3100 44.065 29.02 0.989 0.983 0.973
k141_16888 3100 46.613 6.81 0.985 0.977 0.965
k141_76734 3097 46.529 9.92 0.984 0.973 0.961
k141_43409 3097 52.050 11.17 0.988 0.977 0.964
k141_2611 3097 58.541 12.74 0.979 0.969 0.958
k141_25826 3097 54.246 8.99 0.976 0.964 0.952
k141_195 3097 46.787 12.54 0.992 0.982 0.971
k141_68819 3096 41.505 8.73 0.972 0.966 0.958
k141_5449 3096 50.904 8.23 0.993 0.987 0.979
k141_30197 3095 49.273 6.60 0.986 0.977 0.967
k141_22682 3095 53.376 7.70 0.992 0.986 0.976
k141_6221 3094 32.321 5.01 0.943 0.934 0.921
k141_2082 3094 48.578 8.18 0.994 0.990 0.982
k141_93807 3093 46.848 19.01 0.995 0.986 0.977
k141_43013 3093 48.400 6.82 0.980 0.973 0.963
k141_58582 3092 50.291 5.70 0.991 0.984 0.974
k141_64210 3091 51.019 7.93 0.980 0.965 0.951
k141_50841 3091 56.195 29.74 0.981 0.977 0.969
k141_5049 3091 49.790 8.79 0.979 0.969 0.958
k141_40765 3091 31.834 6.54 0.934 0.922 0.910
k141_27195 3091 56.519 6.26 0.977 0.960 0.944
k141_52177 3090 60.032 7.03 0.974 0.963 0.951
k141_51721 3090 46.278 11.09 0.991 0.981 0.969
k141_49253 3090 48.803 7.47 0.993 0.987 0.976
k141_3353 3090 59.482 5.32 0.977 0.967 0.953
k141_73058 3089 51.182 10.32 0.985 0.974 0.961
k141_42082 3089 46.811 7.64 0.989 0.979 0.969
k141_28325 3089 50.049 7.08 0.993 0.985 0.975
k141_89770 3088 46.567 25.01 0.992 0.989 0.982
k141_90844 3087 35.536 7.01 0.964 0.961 0.955
k141_74806 3087 53.320 6.70 0.989 0.981 0.969
k141_26855 3087 53.191 6.96 0.993 0.984 0.973
k141_22113 3087 47.133 7.83 0.984 0.979 0.970
k141_6073 3086 52.333 6.48 0.989 0.980 0.970
k141_5862 3086 37.492 7.46 0.966 0.958 0.949
k141_45826 3086 51.167 7.92 0.989 0.984 0.975
k141_16311 3086 46.111 7.80 0.991 0.987 0.980
k141_94359 3085 46.872 10.31 0.993 0.988 0.979
k141_23050 3085 51.183 4.02 0.995 0.987 0.975
k141_90094 3084 41.991 14.99 0.986 0.982 0.974
k141_70275 3084 46.271 34.81 0.992 0.987 0.975
k141_12388 3084 47.795 6.41 0.982 0.975 0.965
k141_28535 3082 36.729 8.73 0.966 0.963 0.956
k141_1265 3082 52.920 5.74 0.992 0.985 0.976
k141_41222 3081 56.378 6.21 0.983 0.975 0.964
k141_93589 3080 54.610 6.42 0.991 0.984 0.974
k141_98615 3079 51.120 7.66 0.987 0.980 0.971
k141_16525 3079 44.755 9.40 0.990 0.985 0.976
k141_21686 3078 49.838 7.01 0.995 0.983 0.972
k141_85092 3076 51.463 11.84 0.993 0.984 0.973
k141_83878 3076 53.316 6.23 0.991 0.984 0.971
k141_16365 3076 48.147 7.35 0.993 0.989 0.981
k141_67440 3075 49.431 31.33 0.989 0.983 0.974
k141_53051 3075 40.943 23.72 0.971 0.966 0.957
k141_18518 3075 56.780 7.09 0.974 0.967 0.957
k141_11369 3075 38.276 5.41 0.975 0.973 0.966
k141_68327 3074 29.506 8.11 0.933 0.930 0.922
k141_1380 3074 59.889 8.62 0.976 0.966 0.952
k141_7124 3073 38.432 10.20 0.971 0.966 0.957
k141_32594 3073 51.025 4.62 0.993 0.986 0.976
k141_9110 3072 43.294 7.48 0.986 0.981 0.973
k141_72491 3071 31.879 6.64 0.942 0.935 0.924
k141_55215 3071 58.808 10.39 0.982 0.975 0.965
k141_45780 3071 38.294 159.53 0.971 0.963 0.950
k141_75096 3070 38.143 7.06 0.973 0.970 0.963
k141_3091 3070 53.941 6.40 0.983 0.972 0.958
k141_25350 3070 56.352 7.93 0.986 0.981 0.971
k141_11937 3070 42.508 6.32 0.985 0.979 0.972
k141_77107 3069 53.926 10.27 0.992 0.988 0.980
k141_49786 3069 51.450 7.17 0.994 0.987 0.976
k141_29476 3069 55.002 8.07 0.981 0.966 0.953
k141_77799 3068 37.647 6.97 0.974 0.971 0.963
k141_75715 3068 48.142 8.86 0.989 0.979 0.969
k141_53550 3068 43.351 37.96 0.985 0.977 0.965
k141_40997 3068 60.267 5.70 0.968 0.958 0.946
k141_14501 3067 40.985 6.48 0.978 0.972 0.963
k141_90747 3066 52.511 7.45 0.983 0.977 0.968
k141_58585 3066 49.772 8.85 0.993 0.983 0.974
k141_92217 3064 47.552 14.32 0.993 0.986 0.977
k141_88601 3064 43.342 9.46 0.970 0.964 0.956
k141_86851 3064 50.033 7.31 0.987 0.974 0.961
k141_4753 3064 46.736 37.78 0.986 0.982 0.974
k141_18274 3064 40.960 9.92 0.983 0.978 0.970
k141_36747 3063 48.482 8.34 0.991 0.987 0.980
k141_22722 3063 52.628 6.51 0.991 0.981 0.970
k141_87407 3062 28.805 9.87 0.930 0.927 0.921
k141_17247 3062 50.425 15.05 0.986 0.980 0.973
k141_90308 3061 35.707 4.59 0.962 0.958 0.950
k141_20437 3061 34.237 6.75 0.945 0.934 0.921
k141_86246 3060 50.196 19.50 0.982 0.973 0.959
k141_73944 3060 60.033 6.13 0.971 0.958 0.941
k141_85145 3059 58.320 7.42 0.980 0.969 0.953
k141_83309 3059 44.426 17.80 0.988 0.980 0.969
k141_27039 3058 38.522 7.54 0.975 0.971 0.962
k141_47395 3057 63.363 15.69 0.964 0.954 0.940
k141_80451 3056 45.746 5.07 0.987 0.975 0.964
k141_73020 3056 38.973 7.76 0.968 0.964 0.957
k141_67919 3056 54.777 11.82 0.986 0.978 0.968
k141_92735 3055 46.972 5.65 0.993 0.984 0.974
k141_64422 3055 49.034 16.00 0.994 0.989 0.980
k141_48718 3055 41.538 6.86 0.970 0.962 0.952
k141_62766 3054 50.524 6.25 0.993 0.982 0.970
k141_72817 3052 41.776 4.77 0.956 0.948 0.935
k141_42385 3052 54.161 29.24 0.989 0.981 0.971
k141_37102 3050 46.918 388.04 0.991 0.985 0.974
k141_36912 3049 32.765 6.23 0.953 0.950 0.944
k141_14767 3049 59.134 8.33 0.983 0.973 0.960
k141_41577 3048 47.605 5.28 0.991 0.986 0.976
k141_23540 3048 33.924 13.05 0.938 0.925 0.910
k141_98739 3047 29.865 5.85 0.928 0.919 0.908
k141_2635 3047 52.872 6.64 0.994 0.985 0.973
k141_69472 3046 47.045 5.78 0.988 0.977 0.966
k141_23074 3045 51.067 7.17 0.982 0.971 0.959
k141_15427 3045 45.025 7.58 0.990 0.984 0.975
k141_22099 3044 55.618 5.28 0.970 0.963 0.949
k141_28855 3043 52.613 226.31 0.989 0.982 0.972
k141_13476 3043 45.613 7.45 0.990 0.984 0.975
k141_75758 3042 53.123 184.49 0.994 0.989 0.980
k141_14341 3042 58.481 6.26 0.972 0.959 0.944
k141_1390 3041 48.767 6.34 0.983 0.971 0.960
Displaying first 5000 of 17827 lines

Method: eggNOG MapperCarlos P. Cantalapiedra, Ana Hernandez-Plaza, Ivica Letunic, Peer Bork, Jaime Huerta-Cepas. (2021). eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Molecular Biology and Evolution, msab293, https://doi.org/10.1093/molbev/msab293

Fast genome-wide functional annotation through orthology assignment.

Custom Parameters
  • --itype = metagenome
  • --genepred = prodigal
  • --decorate_gff = yes
Method: Bacterial ribosomal RNA predictor Seemann T. barrnap 0.9 : rapid ribosomal RNA prediction https://github.com/tseemann/barrnap

Barrnap predicts the location of ribosomal RNA genes in genomes. It supports bacteria (5S,23S,16S), archaea (5S,5.8S,23S,16S), metazoan mitochondria (12S,16S) and eukaryotes (5S,5.8S,28S,18S).

Custom Parameters
  • --outseq = alldata_annotation_rrna.fa
Method: ARAGORNLaslett D, Canback B. (2004) ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 32(1):11-16. https://doi.org/10.1093/nar/gkh152

a program to detect tRNA genes and tmRNA genes in nucleotide sequences

Custom Parameters
  • -m
  • -t
  • -l
  • -w
  • -seq
Method: CAT/BAT: tool for taxonomic classification of contigs and metagenome-assembled genomes (MAGs) von Meijenfeldt FAB, Arkhipova K, Cambuy DD, Coutinho FH, Dutilh BE. (2019) Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biology. 20:217. https://doi.org/10.1186/s13059-019-1817-x

Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies.

Custom Parameters
  • database = /mmfs1/projects/rrc_shared/common/references/CAT/20240422_CAT_nr/db
  • taxonomy = /mmfs1/projects/rrc_shared/common/references/CAT/20240422_CAT_nr/tax

Table 1 . Annotation details

Table 1 . Annotation details
Feature Counts
CDS 93522
rRNA 108
tmRNA 17
tmRNA* 4
tRNA 1185

  • Binning assembled contigs

Method: MetaBAT2Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z. (2019) MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 7:e7359. doi: 10.7717/peerj.7359. PMID: 31388474; PMCID: PMC6662567.

A robust statistical framework for reconstructing genomes from metagenomic data

Custom Parameters
  • --saveCls
  • -l
Method: CAT/BAT: tool for taxonomic classification of contigs and metagenome-assembled genomes (MAGs) von Meijenfeldt FAB, Arkhipova K, Cambuy DD, Coutinho FH, Dutilh BE. (2019) Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biology. 20:217. https://doi.org/10.1186/s13059-019-1817-x

Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies.

Custom Parameters
  • database = /mmfs1/projects/rrc_shared/common/references/CAT/20240422_CAT_nr/db
  • taxonomy = /mmfs1/projects/rrc_shared/common/references/CAT/20240422_CAT_nr/tax

Citations